Instead of performing any training to learn the policy beforehand, we propose to learn the AVS policy online to react properly at any environmental condition of the scene (e.g. changes in the furniture), or to cope with the new modality of sensory data, without the need of an ad-hoc training.
This fundamental shift in the methodology is carried out considering the Partially Observable Monte Carlo Planning (POMCP) method [1]}. POMCP has been applied in benchmark problems, such as rocksample, battleship and pocman (partially observable pacman) with impressive results, however, its use for robotic applications is an open and challenging research problem. To the best of our knowledge, this is the first attempt to use POMCP for the AVS problem.
